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Examining predictors of retention with implications for TESTA@Greenwich

Examining predictors of retention with implications for TESTA@Greenwich

Walker, Simon, Abdillahi, Abdillahi, McKenna, Duncan and Molesworth, Catherine (2017) Examining predictors of retention with implications for TESTA@Greenwich. The Journal of Educational Innovation, Partnership and Change, 3 (1). pp. 122-134. ISSN 2055-4990 (doi:https://doi.org/10.21100/jeipc.v3i1.607)

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Abstract

Student attrition is a pressing issue that universities across the world are attempting to solve. More recently, there has been a focus on retaining students from so-called ‘hard to reach’ groups. This paper proposes a methodology that investigates potential predictors of retention within the context of a particular institution, with the aim of defining who our ‘hard to reach’ students might be. It includes TESTA, a feedback and assessment enhancement process, as an independent variable to determine whether this particular enhancement initiative had any positive effects on retention. The authors use a statistical technique that permits a comparison of retention within the same programmes, pre- versus post-TESTA, relative to background changes in retention in programmes without TESTA over the same timescale. The results of our analysis revealed that of the 10 predictors selected, the following were statistically significant: age group; gender; ethnicity; highest qualification on entry; academic session. These form the basis of our definition of who are ‘hard to reach’ students at our university. Whilst TESTA had no statistically significant impact on retention, several important implications for making specific interventions through TESTA in relation to assessment design and delivery to increase retention are discussed.

Item Type: Article
Additional Information: Last author (CM)'s contribution: Advised on statistical analysis, supervised AA who conducted the analysis, wrote sections relating to statistics and the interpretation thereof, created data set (excluding TESTA data) using business intelligence software. CM was supported by HEFCE grant to Christine Couper (project code K46). *** All works are covered by the Creative Commons license - CC BY-NC-ND - Attribution-NonCommercial-NoDerivs
Uncontrolled Keywords: TESTA, student retention, demographic, regression, hard to reach
Subjects: L Education > L Education (General)
Faculty / Department / Research Group: Faculty of Education & Health
Faculty of Education & Health > Department of Education & Community Studies
Last Modified: 30 Jun 2018 01:27
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT a
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/20155

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